Fractal Image Compression Based on Particle Swarm Optimization and Chaos Searching

Fractal image compression explores the self-similarity property of a natural image and utilizes the partitioned iterated function system (PIFS) to encode it. This technique is of great interest both in theory and application. However, it is time-consuming in the encoding process and such drawback renders it impractical for real time applications. The time is mainly spent on the search for the best-match block in a large domain pool. In order to solve the high complexity of the conventional encoding scheme for fractal image compression, a Chaotic particle swarm optimization (CPSO), based on the characteristics of fractal and partitioned iterated function system (PIFS) is proposed in this paper. Simulations show that the encoding time of our method is over 125 times faster than that of the full search method, while the retrieved Lena image quality is still acceptable.